Titlebook: Machine Learning and Knowledge Discovery in Databases. Research Track; European Conference, Nuria Oliver,Fernando Pérez-Cruz,Jose A. Lozano

箴言

Expertise

安抚

Deep Adaptive Multi-intention Inverse Reinforcement Learningled experts’ demonstrations. For this purpose, we employ the tools from Dirichlet processes and propose an adaptive approach to simultaneously account for both complex and unknown number of reward functions. Using the conditional maximum entropy principle, we model the experts’ multi-intention behav

独特性

Unsupervised Task Clustering for Multi-task Reinforcement Learningt are not limited to a single task. However, most existing approaches implicitly assume a uniform similarity between tasks. We argue that this assumption is limiting in settings where the relationship between tasks is unknown a-priori. In this work, we propose a general approach to automatically clu

Modicum

无目标

Dropout’s Dream Land: Generalization from Learned Simulators to Reality of Reinforcement Learning environments. In some cases, a World Model offers an agent the opportunity to learn entirely inside of its own dream environment. In this work we explore improving the generalization capabilities from dream environments to real environments (Dream2Real). We present a gener

罐里有戒指

Goal Modelling for Deep Reinforcement Learning Agentsis necessary to ensure that the agent’s goals are aligned with the goals of the agent designers to avoid unexpected or unwanted agent behavior. In this work, we propose using Goal Net, a goal-oriented agent modelling methodology, as a way for agent designers to incorporate their prior knowledge rega

公式

消瘦

Affable

Hassan Saber,Léo Saci,Odalric-Ambrym Maillard,Audrey Durandor cells of autoimmunity, or as effector cells of B-cell destruction. In the spontaneously diabetic NOD mouse, macrophages and dendritic cells are the first cells infiltrating at perivascular and peri-insular regions in early insulitis beginning at the age of 3 wk.. These cells start to invade the i